种族敏感的生物医学嵌入可以改善医疗预测模型吗?
Hao Liu1, Nour Moustafa-Fahmy2, Casey Ta1
1Department of Biomedical Informatics.
概括
这项研究发现,将种族分布数据纳入生物医学嵌入式并没有持续改善住院时间或ICU再入院预测. 在健康预测模型中,种族敏感嵌入显示了与中性嵌入相似的性能.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算语言学 计算语言学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 生物医学嵌入对于分析健康数据至关重要.
- 以前的研究表明,人口统计数据可以提高预测模型的准确性.
- 种族分布权重在嵌入中的影响仍未得到充分探索.
研究的目的:
- 开发和评估一个算法来权衡生物医学嵌入中的种族分布.
- 评估种族敏感嵌入是否可以提高预测住院时间 (LHS) 和重症监护室 (ICU) 再入院的时间.
- 复制有关人口统计学敏感嵌入在医疗保健中的实用性的发现.
主要方法:
- 提取了12864个PubMed摘要 (2000-2022) 并链接到临床试验.gov以获取种族分布数据.
- 经过训练的Word2vec和BERT嵌入使用种族加权和中立方法.
- 评估了使用MIMIC-IV电子健康记录数据预测LHS和ICU再接收的嵌入性能.
主要成果:
- 对于LHS预测而言,种族敏感嵌入显示了与中性嵌入相似的性能 (MAE 1.975与2.008).
- 对于ICU再入院预测的准确性 (74.61%与75.17%) 和AUC (0.775) 在种族敏感和中立嵌入之间是相似的.
- 在种族敏感嵌入时,没有观察到对预测准确性的持续显著改善.
结论:
- 与种族分布数据对生物医学嵌入物的权衡本身并不能提高LHS或ICU再接收等健康结果的预测准确性.
- 这些发现挑战了之前的报道,这些报道表明,在医疗保健中对人口统计学敏感的嵌入有显著的好处.
- 需要进一步的研究来理解人口统计数据和预测模型性能之间的复杂相互作用.
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